{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04b86393",
   "metadata": {},
   "source": [
    "# 第1章 感知数据 #"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a7003746",
   "metadata": {},
   "source": [
    "![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75145e86",
   "metadata": {},
   "source": [
    "## 1.0 了解数据科学项目 ##"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47583f17",
   "metadata": {},
   "source": [
    "![数据科学项目过程.png](数据科学项目过程.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a94c63",
   "metadata": {},
   "source": [
    "### 1. 理解商业问题 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8223add",
   "metadata": {},
   "source": [
    "把业务中的描述性语言表述转变为**数据问题**，核心就是建立与业务系统对应的**数据指标体系** 。  \n",
    "<img src=\"image/01.jpg\" style=\"zoom:70%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44554425",
   "metadata": {},
   "source": [
    "###  2.数据收集 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e06c363",
   "metadata": {},
   "source": [
    "<img src=\"image/03.jpg\" style=\"zoom:70%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f21a911",
   "metadata": {},
   "source": [
    "### 3.特征工程 ###  \n",
    "**特征**：在机器学习和大数据领域，一般是指二维数据的**列**。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d615f3f",
   "metadata": {},
   "source": [
    "- 数据清理\n",
    "   - 选择子集\n",
    "   - 列名重名名\n",
    "   - 删除重复值\n",
    "   - 缺失值处理\n",
    "   - 一致化处理\n",
    "   - 数据排序\n",
    "   - 异常值处理\n",
    "   \n",
    "- 特征变换\n",
    "  - 分类型\n",
    "  - 二值型\n",
    "  - 顺序型\n",
    "  - 数值型\n",
    "\n",
    "- 特征选择\n",
    "  - 降低特征维度\n",
    "  - 选取对建立模型具有关键作用的特征\n",
    "  \n",
    "- 特征抽取\n",
    "  - 将数据集上的特征数由多变少，而不丢失特征信息\n",
    "  - 降维"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868ce64f",
   "metadata": {},
   "source": [
    "### 4.评估 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab5a452",
   "metadata": {},
   "source": [
    "- 训练集\n",
    "- 测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a59bc39e",
   "metadata": {},
   "source": [
    "### 5.部署和应用 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d13dc9",
   "metadata": {},
   "source": [
    "部署到商业系统中，对未来进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31c14f1",
   "metadata": {},
   "source": [
    "## 1.1 文件中的数据 ##"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea64f2b",
   "metadata": {},
   "source": [
    "### 1.1.1 CSV 文件 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97cc4ff5",
   "metadata": {},
   "source": [
    "#### 1.使用CSV模块 ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86c6ae21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "csv_file = 'data/cities.csv'\n",
    "f = open(csv_file, 'r')\n",
    "data = csv.reader(f)\n",
    "for line in data:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739fac5b",
   "metadata": {},
   "source": [
    "#### 2.使用pandas库 ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebfd178e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "# df = pd.read_csv('data/cities.csv', names=['name1', 'area1', 'population1', 'longd1', 'latd1'])\n",
    "df = pd.read_csv('data/cities.csv', index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87f61126",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc317f5",
   "metadata": {},
   "source": [
    "#### 3.项目案例 ####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d70278",
   "metadata": {},
   "source": [
    "读取 diaetes.csv数据使用如下方法：\n",
    "- df.shape\n",
    "- df.head()\n",
    "- df.info()\n",
    "- df.dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a1bd55d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv('./data/diabetes.csv')\n",
    "\n",
    "#查看数据集的维度,返回元组\n",
    "df.shape\n",
    "\n",
    "#查看数据集前n行,默认前5行,返回DataFrame\n",
    "df.head()\n",
    "\n",
    "#查看数据集基本信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b9036f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[False]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428ca534",
   "metadata": {},
   "source": [
    "### 1.1.2 Excel文件 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab6c1a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a26187a",
   "metadata": {},
   "source": [
    "#### 1.读Excel文件 ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cb705ea",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_excel('./data/jiangsu.xls')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c94cf02",
   "metadata": {},
   "source": [
    "#### 2.写Excel文件 ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "919417ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('./data/jiangsu01.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d1e505",
   "metadata": {},
   "source": [
    "#### 3.项目案例 ####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35035b2",
   "metadata": {},
   "source": [
    "- 项目描述：\n",
    "   从国家数据网站（ http://data.stats.gov.cn/ ） 下载“全国居民消费价格分类指数”，并用柱形图表示指数变化。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb98f2a",
   "metadata": {},
   "source": [
    "<img src=\"image/国家统计局.png\" style=\"zoom:70%\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cef8ad95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "cpi = pd.read_excel('./data/月度数据.xls')\n",
    "cpi.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ec2b4ea",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 替换了数据集中列的名称，变为相应的年月\n",
    "cpi.columns= cpi.iloc[1]\n",
    "\n",
    "# 从第2行开始截取以下的数据，左闭右开\n",
    "cpi= cpi[2:]\n",
    "\n",
    "# 删除第12，13，14行的数据\n",
    "cpi.drop([11,12,13,14], axis=0, inplace=True)\n",
    "\n",
    "# 删除'指标'数据列\n",
    "cpi.drop(['指标'], axis=1, inplace=True)\n",
    "\n",
    "# 新建'指标简称'数据列\n",
    "cpi['指标简称']= ['总体消费', '视频消费', '服装', '居住', \n",
    "             '生活服务', '交通通信', '教育娱乐', '医保', '其他']\n",
    "cpi.reset_index(drop=True, inplace=True)\n",
    "cpi.columns.rename('序号', inplace=True)\n",
    "cpi\n",
    "cpi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de98fa84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cpi.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d9694df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for column in cpi.columns[:-1]:\n",
    "    cpi[column] = cpi[column].astype(float)\n",
    "cpi.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b887819",
   "metadata": {},
   "source": [
    "**绘制柱形图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4cdf41b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）\n",
    "plt.rcParams['axes.unicode_minus'] = False   # 步骤二（解决坐标轴负数的负号显示问题）\n",
    "plt.bar(cpi.iloc[5, :-1].index, cpi.iloc[5, :-1].values)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cb39f8",
   "metadata": {},
   "source": [
    "**动手练习**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df02fd73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('data/jiangsu.xls')\n",
    "df['city']=['南京', '无锡', '徐州', '常州', '苏州', '南通', '连云港', '淮安',\n",
    "           '盐城', '扬州', '镇江', '泰州', '宿迁']\n",
    "df.set_index(['name'], inplace=True)\n",
    "df\n",
    "plt.bar(df['city'], df['area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac24634",
   "metadata": {},
   "source": [
    "### 1.1.3 图像文件 ### "
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "8b5e22ef",
   "metadata": {},
   "source": [
    "图像文件：\n",
    "- 位图：后缀名为“.jpg .jiff .png”\n",
    "- 矢量图：后缀名为“.swf .svg .pdf”\n",
    "\n",
    "<img src=\"image/位图矢量图.jpeg\" style=\"zoom:70%\" />\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6986b6c",
   "metadata": {},
   "source": [
    "#### 1.基础知识 ####\n",
    "- Pillow是常用的图像处理库(Python Imaging Library)，简称（PIL),具有强大的图像处理功能。\n",
    "- 官方网站：https://pillow.readthedocs.io/ \n",
    "- 安装：pip install Pillow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bab5062",
   "metadata": {},
   "source": [
    "**使用PIL库**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db7e6d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 导入PIL库中的Image模块\n",
    "from PIL import Image\n",
    "\n",
    "color_image = Image.open('image/wuenda.jpg')\n",
    "color_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cc64561",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#转换为黑白图像(灰度图像)\n",
    "gray_image = color_image.convert(\"L\")\n",
    "gray_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8db733",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换为numpy.ndarray数组类型\n",
    "import numpy as np\n",
    "color_array = np.array(color_image)\n",
    "color_array.shape\n",
    "\n",
    "# 转换灰度图片为数组类型\n",
    "gray_array = np.array(gray_image)\n",
    "gray_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358e0aa3",
   "metadata": {},
   "source": [
    "**使用OpenCV** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e8065f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b86abf4e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "img = cv2.imread('./image/wuenda.jpg', 0)\n",
    "img.shape\n",
    "img\n",
    "plt.imshow(img, cmap='gray', interpolation='bicubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3bdddd8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Image.fromarray(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a9a30a",
   "metadata": {},
   "source": [
    "#### 2.项目案例 ####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8eae971",
   "metadata": {},
   "source": [
    "**(1)项目描述**   \n",
    "> 读取一个图像文件，然后完成如下操作：\n",
    "> - 截取图像的一部分并进行显示\n",
    "> - 对得到的黑白图像进行翻转，并显示翻转后的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefae015",
   "metadata": {},
   "source": [
    "**(2)实现过程**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07979667",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "img = cv2.imread('./image/wuenda.jpg', 0)\n",
    "img.shape\n",
    "# 截取图像的一部分\n",
    "part_img = img[40:150, 70:160]\n",
    "# 显示图像\n",
    "Image.fromarray(part_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "025bac18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "img = 255- img\n",
    "Image.fromarray(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dedc34d",
   "metadata": {},
   "source": [
    "## 1.2 数据库中的数据 ##"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163c323d",
   "metadata": {},
   "source": [
    "常见的数据库主要分为以下两类：\n",
    "- 关系型数据库：Oracle、 MySQL、 Microsoft SQL Server等\n",
    "- 非关系型数据库: MongoDB、Redis、Hbase等"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12820be",
   "metadata": {},
   "source": [
    "#### 1.基础知识 ####"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0ae2d0",
   "metadata": {},
   "source": [
    "（1）显示数据库名称\n",
    "```\n",
    "show databases;\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7942980a",
   "metadata": {},
   "source": [
    "(2)显示数据库中的表名称\n",
    "```\n",
    "use books;\n",
    "show tables;\n",
    "show tables from books;\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ce845ac",
   "metadata": {},
   "source": [
    "(3)显示数据库表的结构\n",
    "```\n",
    "desc mybooks;\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd3267f",
   "metadata": {},
   "source": [
    "(4)查询表的记录条数\n",
    "```\n",
    "select count(1) from mybooks;\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570f0d84",
   "metadata": {},
   "source": [
    "(5)查询记录详情\n",
    "```\n",
    "select * from mybooks;\n",
    "select id, name from mybooks;\n",
    "select * from mybooks order by id desc;\n",
    "select * from mybooks where pub='phei';\n",
    "select * from mybooks where id in (2,5);\n",
    "select * from mybooks where id between 2 and 5;\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4587c777",
   "metadata": {},
   "source": [
    "**使用python访问MYSQL**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a218552",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql\n",
    "mydb = pymysql.connect(host='localhost',\n",
    "                      user='root',\n",
    "                      password='',\n",
    "                      db='books')\n",
    "cursor = mydb.cursor()\n",
    "sql = 'select * from mybooks'\n",
    "cursor.execute(sql)\n",
    "datas = cursor.fetchall()\n",
    "for data in datas:\n",
    "    print(data)\n",
    "cursor.close()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e079812d",
   "metadata": {},
   "source": [
    "#### 2.项目案例 ####"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "bef4e63b",
   "metadata": {},
   "source": [
    "**1.项目描述**  \n",
    "> 在MySQL环境中，创建如下结构的数据库表，名字为cities  \n",
    "> 将cities.xls中的数据写入此表中\n",
    "![image.png](attachment:image.png)\n",
    "> 然后，用Python完成如下查询：\n",
    "> - 查询表中的记录总数\n",
    "> - 只查询name 和area 两个字段的记录\n",
    "> - 查询population字段中最大和最小的记录\n",
    "> - 查询全部记录，并将查询结果按照area字段从大到小地排列"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a64303",
   "metadata": {},
   "source": [
    "导入数据SQL：\n",
    "```\n",
    "LOAD DATA INFILE 'C:/cities.csv' INTO TABLE cities \n",
    "     COLUMNS TERMINATED BY ',' \n",
    "     LINES TERMINATED BY '\\r\\n' IGNORE 1 LINES \n",
    "\t\t\t(name,area,population,longd,latd);\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc999a9",
   "metadata": {},
   "source": [
    "**2.实现过程**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec10d263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.使用python创建数据库表结构\n",
    "import pymysql\n",
    "mydb = pymysql.connect(host='localhost',\n",
    "                      user='root',\n",
    "                      password='',\n",
    "                      db='books')\n",
    "sql_create_table = 'CREATE TABLE cities (id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,name\\\n",
    "                    VARCHAR(20),area FLOAT, population INT, longd FLOAT, latd FLOAT)\\\n",
    "                    ENGINE=InnoDB DEFAULT CHARSET=utf8'\n",
    "mydb.query(sql_create_table)\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25427344",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#2.从CSV文档中读入数据，并写入数据库表cities中\n",
    "import pandas as pd\n",
    "mydb = pymysql.connect(host='localhost',\n",
    "                      user='root',\n",
    "                      password='',\n",
    "                      db='books')\n",
    "cursor = mydb.cursor()\n",
    "path = './data/cities.csv'\n",
    "df = pd.read_csv(path)\n",
    "df\n",
    "sql_insert= 'insert into cities (name, area, population, longd, latd) \\\n",
    "            values (\"%s\", \"%s\",\"%s\",\"%s\",\"%s\")'\n",
    "for idx in df.index:\n",
    "    row = df.iloc[idx]\n",
    "    cursor.execute(sql_insert %(row['name'],\n",
    "                        row['area'],\n",
    "                        row['population'],\n",
    "                        row['longd'],\n",
    "                        row['latd']))\n",
    "mydb.commit()\n",
    "cursor.close()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bda5c4c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((9, 'Yancheng', 16972.4, 7260240, 120.13, 33.38), (3, 'Xuzhou', 11764.9, 8580500, 117.2, 34.26), (8, 'Huaian', 9949.97, 4799889, 119.15, 33.5), (13, 'Suqian', 8555.0, 4715553, 118.3, 33.96), (5, 'Soochow', 8488.42, 10465994, 120.62, 31.32), (6, 'Nantong', 8001.0, 7282835, 120.86, 32.01), (7, 'Lianyungang', 7615.29, 4393914, 119.16, 34.59), (10, 'Yangzhou', 6591.21, 4459760, 119.42, 32.39), (1, 'Nanjing', 6582.31, 8004680, 118.78, 32.04), (12, 'Taizhou', 5787.26, 4618558, 119.9, 32.49), (2, 'Wuxi', 4787.61, 6372624, 120.29, 31.59), (4, 'Changzhou', 4384.57, 4591972, 119.95, 31.79), (11, 'Zhenjiang', 3840.32, 3113384, 119.44, 32.2))\n"
     ]
    }
   ],
   "source": [
    "# 3.利用Python语言完成查询要求\n",
    "import pandas as pd\n",
    "mydb = pymysql.connect(host='localhost',\n",
    "                      user='root',\n",
    "                      password='',\n",
    "                      db='books')\n",
    "cursor = mydb.cursor()\n",
    "sql_query = 'SELECT * FROM cities Order by area DESC'\n",
    "cursor.execute(sql_query)\n",
    "data = cursor.fetchall()\n",
    "print(data)\n",
    "cursor.close()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "48a06073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>longd</th>\n",
       "      <th>latd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yancheng</td>\n",
       "      <td>16972.40</td>\n",
       "      <td>7260240</td>\n",
       "      <td>120.13</td>\n",
       "      <td>33.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xuzhou</td>\n",
       "      <td>11764.90</td>\n",
       "      <td>8580500</td>\n",
       "      <td>117.20</td>\n",
       "      <td>34.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Huaian</td>\n",
       "      <td>9949.97</td>\n",
       "      <td>4799889</td>\n",
       "      <td>119.15</td>\n",
       "      <td>33.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Suqian</td>\n",
       "      <td>8555.00</td>\n",
       "      <td>4715553</td>\n",
       "      <td>118.30</td>\n",
       "      <td>33.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Soochow</td>\n",
       "      <td>8488.42</td>\n",
       "      <td>10465994</td>\n",
       "      <td>120.62</td>\n",
       "      <td>31.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nantong</td>\n",
       "      <td>8001.00</td>\n",
       "      <td>7282835</td>\n",
       "      <td>120.86</td>\n",
       "      <td>32.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lianyungang</td>\n",
       "      <td>7615.29</td>\n",
       "      <td>4393914</td>\n",
       "      <td>119.16</td>\n",
       "      <td>34.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yangzhou</td>\n",
       "      <td>6591.21</td>\n",
       "      <td>4459760</td>\n",
       "      <td>119.42</td>\n",
       "      <td>32.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nanjing</td>\n",
       "      <td>6582.31</td>\n",
       "      <td>8004680</td>\n",
       "      <td>118.78</td>\n",
       "      <td>32.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Taizhou</td>\n",
       "      <td>5787.26</td>\n",
       "      <td>4618558</td>\n",
       "      <td>119.90</td>\n",
       "      <td>32.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wuxi</td>\n",
       "      <td>4787.61</td>\n",
       "      <td>6372624</td>\n",
       "      <td>120.29</td>\n",
       "      <td>31.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Changzhou</td>\n",
       "      <td>4384.57</td>\n",
       "      <td>4591972</td>\n",
       "      <td>119.95</td>\n",
       "      <td>31.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Zhenjiang</td>\n",
       "      <td>3840.32</td>\n",
       "      <td>3113384</td>\n",
       "      <td>119.44</td>\n",
       "      <td>32.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name      area  population   longd   latd\n",
       "id                                                  \n",
       "9      Yancheng  16972.40     7260240  120.13  33.38\n",
       "3        Xuzhou  11764.90     8580500  117.20  34.26\n",
       "8        Huaian   9949.97     4799889  119.15  33.50\n",
       "13       Suqian   8555.00     4715553  118.30  33.96\n",
       "5       Soochow   8488.42    10465994  120.62  31.32\n",
       "6       Nantong   8001.00     7282835  120.86  32.01\n",
       "7   Lianyungang   7615.29     4393914  119.16  34.59\n",
       "10     Yangzhou   6591.21     4459760  119.42  32.39\n",
       "1       Nanjing   6582.31     8004680  118.78  32.04\n",
       "12      Taizhou   5787.26     4618558  119.90  32.49\n",
       "2          Wuxi   4787.61     6372624  120.29  31.59\n",
       "4     Changzhou   4384.57     4591972  119.95  31.79\n",
       "11    Zhenjiang   3840.32     3113384  119.44  32.20"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接读取到Pandas的DataFrame中\n",
    "import pandas as pd\n",
    "import pymysql\n",
    "mydb = pymysql.connect(host='localhost',\n",
    "                      user='root',\n",
    "                      password='',\n",
    "                      db='books')\n",
    "sql_query = 'SELECT * FROM cities Order by area DESC'\n",
    "df = pd.read_sql_query(sql_query, con=mydb, index_col='id')\n",
    "mydb.close()\n",
    "df"
   ]
  }
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